Many renewable energy providers face significant challenges when scaling distributed solar/wind assets. Traditional IoT or NoSQL architectures (such as AWS DynamoDB) often break down under heavy telemetry workloads, leading to high latency, astronomical storage costs, complex DevOps overhead, and an inability to perform real-time cross-site queries—a bottleneck that hinders grid integration, predictive maintenance, and local compliance.
Through TDengine Cloud, US solar company Nevados seamlessly scaled its 1 GW fleet to over 34,000 trackers while achieving sub-100 ms query latency across billions of telemetry records with zero database maintenance overhead. Read more.
Many high-precision manufacturers and Tier 1 automotive suppliers face significant challenges when processing high-frequency sensor data from complex machinery. Traditional relational or PostgreSQL-based time-series databases often break down under extreme sampling rates (up to 2.5 kHz across thousands of operational signals), leading to massive storage costs, slow query speeds, data silos, and an inability to establish real-time traceability or deploy AI-driven root cause analysis.

Through TDengine, Siemens, and AIT (Austrian Institute of Technology), Austrian automotive supplier TCG UNITECH successfully unified its aluminum high-pressure die-casting operations under a semantically integrated Industrial AI platform—slashing monthly telemetry data volume by 80% (from 2 TB/month to 400 GB/month across 40 machines via 3–5x higher compression), achieving sub-100 ms query latency, and enabling microsecond-level traceability and explainable quality assurance. Read more.

Many mega-scale battery manufacturers and high-tech industrial enterprise facilities face significant challenges when processing massive time-series telemetry from hyper-automated production lines. Traditional relational databases and legacy time-series solutions (such as InfluxDB) often break down under high-concurrency sensor streaming, leading to extreme write latency, skyrocketing hardware costs, severe CPU usage, and an inability to achieve real-time equipment health analysis or millisecond-level quality control.

Through TDengine, global lithium-ion battery leader CATL successfully deployed a high-performance industrial data foundation across its smart manufacturing workshops—effortlessly ingesting over 10 million records per minute across 10,000+ devices and 1 million metrics, while tripling data write speed (from 280,000 to 920,000 points/second), slashing storage costs by 70%, shrinking the required server cluster from 50 nodes down to just 10 (reducing hardware costs by 60%), and boosting predictive maintenance accuracy to 95%. Read more.

Many industrial equipment manufacturers and IoT solution providers face significant challenges when processing high-volume time-series telemetry from remote machinery. Traditional architectures combining relational databases (such as PostgreSQL) with external streaming, queuing, and caching layers (like Flink, Kafka, and Redis) often break down due to high system complexity, heavy server resource consumption, cumbersome deployment workflows, and the need for complex database partitioning as data scales.

Through TDengine, Siemens successfully upgraded its SIMICAS® industrial digital transformation solution—replacing Flink, Kafka, and Redis with TDengine's all-in-one architecture, eliminating the need for database and table partitioning, reducing system complexity and hardware costs, and achieving sub-100 ms P99 latency across key performance indicator (KPI) queries Read more.

Many large-scale enterprise chains and global IT service providers face significant challenges when managing and analyzing massive server monitoring time-series telemetry. Traditional database architectures often break down under heavy daily record ingestion (over 3.5 billion records per day across hundreds of servers), leading to severe query bottlenecks, long processing delays (taking over 2.5 seconds to compute percentile metrics like P90, P95, and P99 across 10 million rows), and an inability to deliver sub-second, real-time analytics required for proactive system anomaly detection.

Through TDengine, global quick-service restaurant leader McDonald's successfully transformed its server monitoring pipeline into a high-performance real-time analytics system—optimizing multi-percentile query execution through merged histogram bucketing and intelligent summary statistics, slashing query response times from 2.5 seconds down to 0.8 seconds for single-table queries over 10 million records, and enabling instant, sub-second operational visibility across 800+ servers generating 3.5 billion data points daily. Read more.

Many biotech enterprises and industrial biomanufacturing leaders face significant challenges when processing high-frequency time-series telemetry from biological reactors and processing equipment. Traditional or alternative open-source time-series databases often break down due to unreliable edge-to-cloud replication, vendor lock-in, missing native high-availability (HA) features, high storage overhead, and restricted query retention windows (e.g., limits as short as 72 hours without expensive enterprise licenses), creating severe bottlenecks for real-time process monitoring, regulatory compliance, and machine learning analytics.

Through TDengine, UK biomanufacturing innovator Extracellular successfully deployed a scalable, future-proof bioprocess data platform—achieving up to 16x faster data ingestion, 21.2x faster query performance, and 50% lower disk storage usage compared to alternative time-series databases, while leveraging native edge-cloud synchronization, out-of-the-box high availability, and automated tiered storage to seamlessly bridge lab R&D with large-scale production.Read more.

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